The client is a global biopharmaceutical company headquartered in Brussels, Belgium. The company’s focus lies in research and development for the treatment of severe diseases, specializing in neurology, immunology, and healthcare innovation beyond medicine.

Our client wanted to develop a Machine Learning (ML)-based tool that could assist chemists in their exploration of drug synthesis methods while efficiently identifying the optimal output and minimizing wastage based on their available inventory.

To address their concerns, we developed a comprehensive Forecasting, Pricing & Revenue Maximization platform that provides access to inventory information and can determine the potential compounds that can be created using the available inventory, considering the quantity, cost, and content of the inventory. Using this system, chemists could also input the desired drug into the system, and it could generate the most optimal pathways for synthesizing it, taking into account the availability of chemicals in stock.

Our solution helped the client to:

• Improve the drug generation process with the available inventory in no time
• Maximize inventory management and minimize the storage cost
• Validate the chemical composition of the generated drugs
• Save 30-40% of time and resources in lab work

Fill out the form to download the detailed case study and know more about how our team was successful in efficiently optimizing the client's inventory management and simplifying their drug synthesis process.